LE and FR, as proposed in [1]}, [2]}, were utilized as evaluation metrics in this work. The LE metric was slightly modified to cope with the discrete grid structure used here. Therefore, the Euclidean distance between a ground-truth speaker position and the center point of a grid cell where a speaker was detected (both represented as pixel coordinates), was utilized. This distance was computed for all possible combinations of detected speakers and ground-truth speaker positions at each time-frame. If fewer speakers were predicted than are actually present, the prediction vector was filled with the next highest activation, to have at least one prediction per target. To calculate the localization error for an estimated speaker position given the corresponding ground-truth, a cost matrix was optimized using the Hungarian algorithm [3]}. The resulting distances were summed up and divided by the total number of speakers present at the particular time-frame, yielding the average distance per speaker. This process is repeated and averaged for the complete test set to obtain the final LE metric.
